Real-world brain imaging in a population-based cohort enables accurate markers for dementia
Bibliographic record
Abstract
Abstract INTRODUCTION While a vast amount of MRI data are collected for healthcare delivery, generating real-world evidence (RWE) in Alzheimer’s and related diseases (ADRD) research is substantially limited by lack of methods and results showing how routine MRIs can be used for ADRD imaging studies. METHODS We compared three established ADRD biomarkers (total gray matter, hippocampal and ventricular volumes) in four groups (normal, subjective complaints, mild cognitive impairment, and dementia) between the general population of women born in 1932-1941 in the Kuopio region of eastern Finland (population-based OSTPRE cohort, N=14220) and a well-characterized research cohort (ADNI). RESULTS A total of 2434 brain MRIs for 1885 women were collected between 2003-2022 by the public healthcare provider covering all residents in the region. The established biomarkers were overall aligned between these cohorts. DISCUSSION Typical biomarkers extracted from real-world brain MRI scans collected over 20 years are suitable for generating RWE in ADRD research. Highlights Real-world brain MRI is applicable for generating evidence in ADRD research First study comparing a real-world MRI cohort with an established research cohort reference A methodological framework for RWE ADRD studies using routinely collected MRIs
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".